The need to extract weak signals from imagery is an impediment to many technical efforts. Prime examples include identifying nascent cancers and cancerous precursors in medical imagery (both 2D and 3D), and exoplanet detection.
In an effort to advance the weak signal extraction field, researchers at leading exoplanet efforts gathered in 2012 to collaborate. Information from the meeting is published in Lawson et al, On advanced estimation techniques for exoplanet detection and characterization using ground-based coronagraphs, SPIE Astronomical Telescopes+Instrumentation, pp. 844722-844722, 2012. A related PowerPoint presentation was delivered at the 2012 SPIE Adaptive Optics Systems III conference. Both the paper and the PowerPoint presentation are attached to application 61/871,772. Among the researchers' efforts was publication by Poyneer, et al, of a suite of simulated speckle data (the NAKFI data), on which different techniques could be tried. Such data is publicly available at http://olbin<dot>jpl<dot>nasa<dot>gov/nakfi, in the form of 100 compressed (.tar) fits files—each corresponding to an hour's worth of observations, as more fully detailed in the Lawson paper. (Fits is a standard data format used in astronomy: Flexible Image Transport System.)
The challenge of finding exoplanets in astronomical imagery is extreme. The planets are dimmer, by a factor of 104-1012, than the stars that they orbit. The angular resolution of even the best contemporary telescopes is modest compared to the scales involved—scales which are generally finer than one arc second of separation between a star and a planet and most often below one tenth of an arc second for stars further away than ten or twenty parsecs. And the speckling phenomena introduced by atmospheric seeing effects causes artifacts that, on first impression, resemble planets.
Applicant has developed techniques that are believed to advance the state of the art in discerning small, exceedingly weak image features. Some of these techniques rely on sensing myriad image gradients, through use of non-linear filtering methods, to look for very tiny biases which a dim companion planet causes to otherwise star-dominant data.
In one exemplary embodiment, the presence or absence of an exoplanet is judged from a large set of “votes” about whether a point in the imagery has a value above or below its neighbors. Hundreds or thousands or more votes can be gathered from one or more images, to thereby—in the aggregate—tend to confirm or refute the presence of an exoplanet at a particular location. Votes can be based on simple pixel differences, e.g., between a pixel at the center of a 7×7 pixel image excerpt, and each of 48 other pixels in the excerpt. More sophisticated methods employ analyses of triads, quads, and rings, and kernel-based Markov Random Field frameworks, to roll-up to a conclusion.
The foregoing and additional features and advantages of the present technology will be more readily apparent from the following Detailed Description, which proceeds with reference to the accompanying drawings.